A Graduate Program in Quantitative Biology
Last updated: August 15, 2024 at 11:12 AM
Objectives
The quantitative biology specialization is available only to students enrolled and working toward the MS or PhD degree in one of the six participating graduate programs: Biochemistry and Biophysics, Chemistry, Computer Science, Molecular and Cell Biology, Neuroscience, and Physics. Individuals who want to obtain an MS or PhD degree with a specialization in quantitative biology (QB) should apply to one of the participating MS or PhD programs as described in the relevant section of this Bulletin. Enrolled graduate students who want to obtain the quantitative biology specialization should contact their graduate program chair or quantitative biology liaison for further information. Students wishing to obtain the specialization are advised also to contact one of the quantitative biology co-chairs for information about participating in the noncurricular educational activities of the quantitative biology program.
Faculty Advisory Committee
Bruce Goode, Co-Chair, Liaison to Molecular and Cell Biology PhD Program
(Biology)
Jané Kondev, Co-Chair, Liaison to Physics PhD Program
(Physics)
Alexandre Bisson, Liaison to Biology PhD Program
(Biology)
Thomas Fai, Liaison to Mathematics PhD Progam
(Mathematics)
Paul Garrity, Liaison to Biology PhD Program
(Biology)
Paul Miller, Liaison to Neuroscience PhD Program
(Biology)
Avital Rodal, Liaison to Biology PhD Program
(Biology)
Stephen Van Hooser, Liaison to Biology PhD Program
(Biology)
Hannah Yevick, Liaison to Physics PhD Program
(Physics)
Requirements for the Specialization to the Degree of Master of Science or Doctor of Philosophy
Students must complete all requirements for the degree of Master of Science or Doctor of Philosophy in the program in which they are enrolled. In addition, students must successfully complete one course from three of the following four areas:
1. Quantitative Biology Lab
QBIO 120b Quantitative Biology Instrumentation Laboratory
Director-approved quantitative biology lab course offered outside of Brandeis. Examples include:
- Cold Spring Harbor Laboratory courses in Quantitative Imaging
- Marine Biological Laboratory courses, such as:
- Physiology: Modern Cell Biology Using Microscopic, Biochemical and Computational Approaches
- Analytical and Quantitative Light Microscopy; Optical Microscopy and Imaging in the Biomedical Sciences
2. Quantitative Approaches to Biology
BCHM 102a Quantitative Approaches to Biochemical Systems
BIOL 103b Mechanisms of Cell Functions
NBIO 240a Principles of Neuroscience Research
3. Mathematical Modeling of Biological Systems
PHYS 105a Biological Physics
MATH 123a Principles of Mathematical Modeling
BCHM 145a How to Decide: Bayesian Inference and Computational Statistics
QBIO 110a Numerical Modeling of Biological Systems
4. Computational Methods in Biology
BIOL 107a Data Analysis and Statistics Workshop
NBIO 136b Computational Neuroscience
QBIO 110a Numerical Modeling of Biological Systems
COSI 178a Computational Molecular Biology
BIOL 131b Introduction to Genomics
MATH 232a (formerly MATH 162a) Numerical Methods for Scientific Computing
Courses of Instruction
(1-99) Primarily for Undergraduate Students
QBIO
11a
Nature's Nanotechnology
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Familiarity with high school math, physics, chemistry and biology is expected. Enrollment limited to QBReC Scholars. Formerly offered as FYS 11a.
Imagine a world occupied by machines whose size is 10,000 times smaller than the width of a human hair. Some of them produce fuel by harnessing solar energy, while others transport cargo on tracks only 10 atoms across, or assemble other machines following molecular blueprints. This is the bustling world inside a living cell, which we will explore using high school level math, physics and biology. Usually offered every year.
QBIO
24b
QBReC Lab
Prerequisite: QBIO 11a. Yields half-course credit. Formerly offered as EL 24b.
Students explore the living world through experimental and computational projects conducted in research labs. The emphasis is on interdisciplinary science where techniques from physics, chemistry and biology are used to develop a quantitative understanding of life at the molecular and cellular level. Usually offered every year.
(100-199) For Both Undergraduate and Graduate Students
BIOL
103b
Mechanisms of Cell Functions
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Prerequisite: BIOL 100b.
Focuses on the mechanistic basis of cell biological processes, with a heavy emphasis on how they are elucidated experimentally. Classic and modern research papers are used to illustrate a range of genetic, biochemical, and imaging-based experimental approaches. Topics include cell compartmentalization, membrane traffic, cytoskeleton, cell motility, and cell division. The primary learning goal is to understand how the scientific method is applied in cell biology research. Intended for graduate students and advanced undergraduates. Usually offered every year.
BIOL
131b
Introduction to Genomics
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Prerequisites: BIOL 14a and BIOL 15b.
Focuses on the rapidly developing field of Genomics. During the course, the students will be introduced to general concepts, technologies, and approaches for generating and analyzing genomic datasets. The specific applications will include the analysis of large-scale neurogenomics datasets. Usually offered every year.
MATH
123a
Principles of Mathematical Modeling
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Prerequisites: MATH 15a or MATH 22a, MATH 20a or MATH 22b, and MATH 37a.
Provides the basic concepts and approaches for modeling in physics and biology. The course will be developed around examples of central research interest in biology and related fields. Usually offered every second year.
QBIO
110a
Numerical Modeling of Biological Systems
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Prerequisite: MATH 10a and b or equivalent.
Modern scientific computation applied to problems in molecular and cell biology. Covers techniques such as numerical integration of differential equations, molecular dynamics and Monte Carlo simulations. Applications range from enzymes and molecular motors to cells. Usually offered every second year.
QBIO
120b
Quantitative Biology Instrumentation Laboratory
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Focuses on optical and other instruments commonly used in biomedical laboratories to make quantitative measurements in vivo and in vitro. Students disassemble and reconfigure modular instruments in laboratory exercises that critically evaluate instrument reliability and usability and investigate the origins of noise and systematic error in measurements. Usually offered every year.
(200 and above) Primarily for Graduate Students
NBIO
240a
Principles of Neuroscience Research
Prerequisites: One year of college-level chemistry with lab, one year of college-level physics with lab, and any math course above 10a, b.
A lecture- and literature-based course examining the fundamental principles of neuroscience. Lecture topics include ion channel biophysics, resting potentials, action potentials, synaptic transmission, sensory systems, motor systems, learning, neural circuits underlying behavior, and neuropsychiatric diseases. Complementary readings of classical and current primary literature will give a deeper understanding of the fundamental underpinnings of nervous system structure and function. Intended for PhD students and advanced undergraduates or masters students who intend to perform basic research in neuroscience. Usually offered every year.
QBIO Cross-Listed Courses
BCHM
102a
Quantitative Approaches to Biochemical Systems
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Prerequisite: BCHM 100a or equivalent and Math 10a and b or equivalent.
Introduces quantitative approaches to analyzing macromolecular structure and function. Emphasizes the use of basic thermodynamics and single-molecule and ensemble kinetics to elucidate biochemical reaction mechanisms. Also discusses the physical bases of spectroscopic and diffraction methods commonly used in the study of proteins and nucleic acids. Usually offered every year.
BIOL
107a
Data Analysis and Statistics Workshop
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Prerequisites: BIOL51a, high school statistics, or similar course.
The interpretation of data is key to making new discoveries, making optimal decisions, and designing experiments. Students will learn skills of data analysis and computer coding through hands-on, computer-based tutorials and exercises that include experimental data from the biological sciences. Usually offered every year.
MATH
232a
Numerical Methods for Scientific Computing
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Prerequisites: MATH 37a and MATH 122a, or permission of the instructor. A basic proficiency with a programming language such as Python or Matlab is required.
Studies numerical methods for linear algebra, ordinary and partial differential equations, and optimization. Equal emphasis will be placed on theory (stability, accuracy, and convergence) and practical problem-solving using a programming language such as Python. Usually offered every second year.
NBIO
136b
Computational Neuroscience
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Prerequisites: MATH 10a or higher and one of the following: NBIO 140b/240b, PHYS 10b/11b/15b, BIOL 107a, or any COSI course.
An introduction to concepts and methods in computer modeling and analysis of neural systems. Topics include single and multicompartmental models of neurons, information representation and processing by populations of neurons, synaptic plasticity and models of learning, working memory, decision making and neural oscillations. The course will be based on in-class computer tutorials, assuming limited prior coding experience, with reading assignments and preparation as homework. Usually offered every second year.
PHYS
105a
Biological Physics
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Physical forces in living matter are studied from the perspective offered by statistical mechanics, elasticity theory, and fluid dynamics. Quantitative models for biological structure and function are developed and used to analyze systems such as single molecule experiments, transcriptional regulation networks, the forces arising during DNA packaging in a virus, and mechanisms underlying mammalian pattern formation. Usually offered every second year.
QBIO
120b
Quantitative Biology Instrumentation Laboratory
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Focuses on optical and other instruments commonly used in biomedical laboratories to make quantitative measurements in vivo and in vitro. Students disassemble and reconfigure modular instruments in laboratory exercises that critically evaluate instrument reliability and usability and investigate the origins of noise and systematic error in measurements. Usually offered every year.